We follow the same data splits as in SAT [1]} on all benchmark datasets, including the split of attribute-observed/-missing nodes and the spilt of train/test sets. For GINN [2]} and GCNMF [3]}, we set the hyper-parameters of both methods by following their original papers. For other compared methods, we report the results listed in the paper of SAT directly. For our SAGA, we adopt 4-layer GCNs as our backbone and train it with Adam optimizer, where the learning rate is set to 1e-3. We first conduct profiling task using Recall@K and NDCG@K as metrics to evaluate the quality of restored attributes by training SAGA for at least 500 iterations until convergence. Then we utilize a GCN-based classifier to perform node classification task over the rebuilt graph with five-fold validation in 10 times, and report the averages evaluated by accuracy (ACC). We adopt an early stop strategy when the loss value comes to a plateau to avoid over-fitting. According to the results of hyper-parameter analysis, we set the hyper-parameters \(P\) , \(K\) , and \(\gamma \)  as 5, and fix \(\lambda \)  to 10. More implementation details are presented in Appendix A.
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